Sovereign AI: Rethinking Autonomy in the Age of Global Interdependence

TL;DR

This work reframes AI sovereignty as a continuum balancing autonomy with interdependence across Data, Compute, Models, and Norms, rather than a binary claim of national control. It introduces a formal planner’s model where sovereignty returns are allocated under a budget, with data and compute exhibiting strong complementarity that enhances model autonomy, and openness providing diminishing returns with exposure risk. The authors derive a tractable set of analytical results via a four-ppillar framework, a complementarity term, and an openness function, then apply the model to India and the Middle East to yield concrete policy prescriptions such as pairing data with compute, implementing robust ModelOps, and maintaining governance guardrails. The key practical contribution is a measurable, policy-ready approach to managed interdependence, including a simple openness rule and quarterly dashboards to monitor marginal sovereignty returns, enabling governments to balance sovereignty objectives with global collaboration in a rapidly interlinked AI ecosystem.

Abstract

Artificial intelligence (AI) is emerging as a foundational general-purpose technology, raising new dilemmas of sovereignty in an interconnected world. While governments seek greater control over it, the very foundations of AI--global data pipelines, semiconductor supply chains, open-source ecosystems, and international standards--resist enclosure. This paper develops a conceptual and formal framework for understanding sovereign AI as a continuum rather than a binary condition, balancing autonomy with interdependence. Drawing on classical theories, historical analogies, and contemporary debates on networked autonomy, we present a planner's model that identifies two policy heuristics: equalizing marginal returns across the four sovereignty pillars and setting openness where global benefits equal exposure risks. We apply the model to India, highlighting sovereign footholds in data, compute, and norms but weaker model autonomy. The near-term challenge is integration via coupled Data x Compute investment, lifecycle governance (ModelOps), and safeguarded procurement. We then apply the model to the Middle East (Saudi Arabia and the UAE), where large public investment in Arabic-first models and sovereign cloud implies high sovereignty weights, lower effective fiscal constraints, and strong Data x Compute complementarities. An interior openness setting with guardrails emerges as optimal. Across contexts, the lesson is that sovereignty in AI needs managed interdependence, not isolation.